Privacy-Aware Infrastructure in the AI-Native Era: An Asset Classification Case Study

What this post added

Introduces a hybrid pattern for asset classification within privacy-aware infrastructure (PAI) to handle the complexities of AI-native products. This pattern leverages LLMs for ambiguity and novelty, distills stable behavior into deterministic, versioned rules for enforcement, and separates human-reviewed labels from model-generated recommendations. The post details the operational concerns of PAI (Understand, Discover, Enforce, Demonstrate) and the challenges of asset classification, proposing a three-principle approach: context over prompts, decoupled evaluation from optimization, and distilling stable behavior into deterministic rules. It outlines a seven-stage practical process, including defining a stable contract, building a context mesh, and a two-lane operating pattern for deterministic and LLM-based decision-making.